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{{alias}}( [options] )
Returns an accumulator function which incrementally performs Grubbs' test
for detecting outliers.
Grubbs' test assumes that data is normally distributed. Accordingly, one
should first verify that the data can be reasonably approximated by a normal
distribution before applying the Grubbs' test.
If provided a value, the accumulator function returns updated test results.
If not provided a value, the accumulator function returns the current test
results.
If provided `NaN` or a value which, when used in computations, results in
`NaN`, the test statistic is `NaN` for all future invocations.
The accumulator must be provided *at least* three data points before
performing Grubbs' test. Until at least three data points are provided, the
accumulator returns `null`.
The accumulator function returns an object having the following fields:
- rejected: boolean indicating whether the null hypothesis should be
rejected.
- alpha: significance level.
- criticalValue: critical value.
- statistic: test statistic.
- df: degrees of freedom.
- mean: sample mean.
- sd: corrected sample standard deviation.
- min: minimum value.
- max: maximum value.
- alt: alternative hypothesis.
- method: method name.
- print: method for pretty-printing test output.
Parameters
----------
options: Object (optional)
Function options.
options.alpha: number (optional)
Significance level. Default: 0.05.
options.alternative: string (optional)
Alternative hypothesis. The option may be one of the following values:
- 'two-sided': test whether the minimum or maximum value is an outlier.
- 'min': test whether the minimum value is an outlier.
- 'max': test whether the maximum value is an outlier.
Default: 'two-sided'.
options.init: integer (optional)
Number of data points the accumulator should use to compute initial
statistics *before* testing for an outlier. Until the accumulator is
provided the number of data points specified by this option, the
accumulator returns `null`. Default: 100.
Returns
-------
acc: Function
Accumulator function.
Examples
--------
> var acc = {{alias}}();
> var res = acc()
null
> for ( var i = 0; i < 200; i++ ) {
... res = acc( {{alias:@stdlib/random/base/normal}}( 10.0, 5.0 ) );
... };
> res.print()
References
----------
- Grubbs, Frank E. 1950. "Sample Criteria for Testing Outlying
Observations." _The Annals of Mathematical Statistics_ 21 (1). The Institute
of Mathematical Statistics: 27–58. doi:10.1214/aoms/1177729885.
- Grubbs, Frank E. 1969. "Procedures for Detecting Outlying Observations in
Samples." _Technometrics_ 11 (1). Taylor & Francis: 1–21. doi:10.1080/
00401706.1969.10490657.
See Also
--------
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